Drug and Alcohol Dependence 111 (2010) 4–12
Contents lists available at ScienceDirect
Drug and Alcohol Dependence
journal homepage: www.elsevier.com/locate/drugalcdep
Review
Measuring addiction propensity and severity: The need for a new instrument!
Kevin P. Conway a,∗ , Janet Levy b , Michael Vanyukov c , Redonna Chandler a ,
Joni Rutter d , Gary E. Swan e , Michael Neale f
a
Division of Epidemiology, Services, and Prevention Research, National Institute on Drug Abuse, National Institutes of Health, Department of Health and Human Services,
6001 Executive Blvd., Room 5153, MSC 9589, Bethesda, MD 20892-9589, USA
b
Duke University School of Nursing, SON New Building, Room 1019, 307 Trent Drive, Durham, NC 27710, USA
c
Departments of Pharmaceutical Sciences, Psychiatry and Human Genetics, University of Pittsburgh, SALK 528, Pittsburgh, PA 15260, USA
d
Division of Basic Neuroscience and Behavioral Research, National Institute on Drug Abuse, National Institutes of Health, Department of Health and Human Services,
6001 Executive Blvd., Room 4282, MSC 9589, Rockville, MD 20852, USA
e
Center for Health Sciences, SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025, USA
f
Department of Psychiatry, Virginia Commonwealth University, 1200 East Broad Street, PO Box 980710, Richmond, VA 23298-0710, USA
a r t i c l e
i n f o
Article history:
Received 30 June 2009
Received in revised form 1 March 2010
Accepted 2 March 2010
Available online 11 May 2010
Keywords:
Tobacco
Cannabis assessment
Individual differences
Adolescents
a b s t r a c t
Drug addiction research requires but lacks a valid and reliable way to measure both the risk (propensity)
to develop addiction and the severity of manifest addiction. This paper argues for a new measurement
approach and instrument to quantify propensity to and severity of addiction, based on the testable
assumption that these constructs can be mapped onto the same dimension of liability to addiction. The
case for this new direction becomes clear from a critical review of empirical data and the current instrumentation. The many assessment instruments in use today have proven utility, reliability, and validity,
but they are of limited use for evaluating individual differences in propensity and severity. The conceptual and methodological shortcomings of instruments currently used in research and clinical practice can
be overcome through the use of new technologies to develop a reliable, valid, and standardized assessment instrument(s) to measure and distinguish individual variations in expression of the underlying
latent trait(s) that comprises propensity to and severity of drug addiction. Such instrumentation would
enhance our capacity for drug addiction research on linkages and interactions among familial, genetic,
psychosocial, and neurobiological factors associated with variations in propensity and severity. It would
lead to new opportunities in substance abuse prevention, treatment, and services research, as well as in
interventions and implementation science for drug addiction.
Published by Elsevier Ireland Ltd.
Contents
1.
2.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Severity measurement instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.
Addiction Severity Index (ASI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.
Drug Use Screening Inventory (DUSI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.
Global Appraisal of Individual Needs (GAIN) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.
Severity of Alcohol Dependence Questionnaire (SADQ) and Alcohol Dependence Scale (ADS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.5.
Severity of Opioid Dependence Questionnaire (SODQ), the Severity of Amphetamine Questionnaire (SamDQ), and the Benzodiazepine
Dependence Questionnaire (BDEPQ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6.
Chemical Use Abuse and Dependence (CUAD) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.7.
Substance Dependence Severity Scale (SDSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8.
Severity of Dependence Scale (SDS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.9.
Substance Use Involvement Index (SUII) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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! Disclaimer: The views and opinions expressed in this paper are those of the authors and do not necessarily represent the views of the National Institute on Drug Abuse,
National Institutes of Health, or any other governmental agency.
∗ Corresponding author at: Division of Epidemiology, Services, and Prevention Research (DESPR), National Institute on Drug Abuse, 6001 Executive Blvd., Suite 5185,
Bethesda, MD 20892-9589, USA. Tel.: +1 301 443 6504; fax: +1 301 443 2636.
E-mail address: kconway@nida.nih.gov (K.P. Conway).
0376-8716/$ – see front matter. Published by Elsevier Ireland Ltd.
doi:10.1016/j.drugalcdep.2010.03.011
K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12
3.
4.
Propensity measurement instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.
Transmissible Liability Index (TLI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Summary and future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.
Limitations of existing instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.
Advantages of item-response theory (IRT) for measuring propensity and severity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.
Advantages of an instrument measuring propensity to and severity of addiction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conflict of interest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1. Introduction
Drug addiction research spans the gamut from neuroscience
and genetics to prevention, treatment and services. As in other
biobehavioral disciplines, this research requires a valid and reliable measure of both the propensity to develop addiction and the
severity of manifest addiction. This paper examines the need for
such a measure as well as the assumption on which it is based,
namely that these constructs share the same dimension. It provides a critical review of theory, data, and current instrumentation
to show how a new measurement tool could impact the field of
addiction research. Finally, it considers the next steps required for
the development and fruition of a new instrument for measuring
addiction.
Fueled by technological innovations and multidisciplinary scientific collaborations, addiction science has advanced rapidly over
the past 20 years. The notion that addiction “runs in families”
(Merikangas and Conway, 2009; Bierut et al., 1998; Merikangas
et al., 1998; Reich et al., 1988; Schuckit et al., 1972; Winokur
et al., 1970) is now attributed in large part to additive genetic
factors (Kendler et al., 2003a; Tsuang et al., 1996). This has lead
scientists to search for genes that influence variation in the risk
for addiction, with a number of replication studies now emerging on candidate genes (Foll et al., 2009; Kreek et al., 2005; Li and
Burmeister, 2009; Saxon et al., 2005; Schuckit, 2009; Uhl, 2004;
Vanyukov and Tarter, 2000). Scientific advances in neuroscience
have shown that addiction is a chronic and often relapsing disease
that is linked to pathological changes in neural circuitry, including
those involved in reward and motivation, learning and memory,
cognitive control, decision making, mood, and interoceptive awareness (Adinoff, 2004; Hyman et al., 2006; Kalivas and Volkow,
2005; Kalivas and O’Brien, 2007; Koob and Le, 1997; O’Brien, 2003;
Volkow et al., 2003, 2004). Importantly, these changes involve
the same structures and processes that contribute to the mechanisms of behavior regulation and its deviations, predating drug
use (Vanyukov et al., 2003b). Impairment of the addicted brain
provides a neurological basis for the cardinal behavioral manifestation of drug addiction—persistent drug use despite serious adverse
consequences. Longitudinal observations of addicts demonstrate
the chronic relapsing nature of addiction and the need for longterm treatment strategies (Dennis and Scott, 2007; Dennis et al.,
2005). However, the malleability of the liability phenotype, including “maturing-out” of conditions that meet diagnostic criteria for
dependence (Dawson et al., 2006; Newcomb et al., 2001), underscores the nonspecific character and construct validity of liability
while still holding promise for effective intervention. In contrast
to such static traits as stature in adults, liability to addiction is
dynamic over time and development. It can be viewed as forming an ontogenetic trajectory that can be tracked and measured,
and given its inherent dynamism, potentially changed (Tarter and
Vanyukov, 1991, 1994).
Advances in these and other important areas of addiction
science have outpaced progress in the measurement of addiction, and the ability to quantify and measure this construct
accurately remains a looming methodological challenge (Conway
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et al., 2006; Craddock et al., 2008; Merikangas and Avenevoli,
2000; Merikangas and Conway, 2008; Neale et al., 2006). Currently, research usually measures drug addiction in the broadest
sense by classifying and contrasting “addicts” to “non-addicts”
according to diagnostic criteria. Genetic risk for addiction is similarly categorized based on a parent’s diagnosis or family history.
This approach implicitly assumes categorical distinctions between
groups and categorical similarities among the individuals within
each group regarding their symptoms and group-identified features of addiction. Few studies have focused on variations among
individuals within each group, despite the common knowledge
that meaningful individual differences exist within any given
cohort of addicts and controls. Such heterogeneity is reflected
in the hundreds of different combinations of addiction symptoms that meet diagnostic criteria (Vanyukov et al., 2003b).
Although needed for clinical practice, the diagnosis per se is
less than optimal when it comes to genetic and other etiology
research, or for informing efforts in the primary prevention of
addiction.
A precise method and instrument for measuring individual
variation in liability to addiction is therefore needed to build on
and advance the science and understanding of the multifactorial
nature of drug abuse and addiction. A term introduced in human
genetics by Falconer (1965, p. 52), liability is a latent (unobservable) quantitative trait that, if measured, “would give us a graded
scale of the degree of affectedness or of normality”, with these two
categories divided by a threshold. The “gradations of normality”
(the subthreshold liability phenotypes) correspond to variation in
the risk (propensity), whereas “gradations of affectedness” (the
suprathreshold phenotypes, likely to be assigned a clinical diagnosis) correspond to variation in severity, comprising the two portions
of the liability distribution. Applied to addiction, severity refers
to the degree of maladaptive compulsive drug-seeking and using
behavior displayed by an individual, corresponding to variation
in liability above the diagnostic threshold. Propensity refers to the
probability of the disorder’s onset, corresponding to liability variation below the diagnostic threshold. It follows that individual
differences in severity manifest as different degrees of maladaptive drug-seeking and drug-using behavior. Similarly, variation in
propensity manifest as behavioral precursors of addiction. The ultimate desirable goal of a new instrument of addiction would be to
provide a single scale (see Fig. 1) by which an individual’s liability to addiction (propensity or severity) can be quantified using a
numeric score.
The plausibility of such a single common (versus drug-specific)
liability dimension (latent trait) and the feasibility of its measurement are supported by clinical, neurobiological, genetic, and
statistical findings (Vanyukov et al., 2003a). Liabilities to addictions
to specific drugs are both phenotypically and genetically highly
correlated, with minimal specific genetic variance; most of the variance in addiction liability is associated with common (thus, likely,
brain-behavioral) mechanisms rather than with drug action per se
(Kendler et al., 2007; Tsuang et al., 1998). Indeed, neurobiological data pertaining to drug reward suggest that many drug effects
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K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12
Fig. 1. Addiction liability distribution.
involve the dopaminergic and other major neurobiological systems, despite differences in specific routes of administration, the
biotransformation pathways of different drugs, or in their primary
targets (Koob and Volkow, 2009). These same systems substantially
overlap with those involved in mechanisms of natural reward and
incentive motivation, stress response, and social behavior as well
(Goldstein and Volkow, 2002; Koob and Volkow, 2009; Vanyukov
et al., 2003a). Moreover, data increasingly show that drug addiction can be located on the same dimension as premorbid (and even
pre-drug use) behaviors that indicate a highly heritable latent trait
variably known as dysregulation, disinhibition, behavior undercontrol or externalizing behavior, including risks for disruptive
behavior disorders (Button et al., 2006; Hicks et al., in press; Kendler
et al., 2007; Kirisci et al., 2009; Krueger et al., 2002; Tarter et al.,
1990, 2003, 2004; Vanyukov et al., 2009; Young et al., 2009). Further support comes from research on an instrument designed to
measure variation in propensity to addiction (the Transmissible
Liability Index, TLI; described in detail below). Comprised largely
of items indicating dysregulation, scores on the TLI have been
found to be elevated among offspring of drug abusers (Tarter et
al., 2003), highly heritable (Vanyukov et al., 2009; Hicks et al., in
press), predictive of addiction subsequent to drug use initiation
(Vanyukov et al., 2009; Kirisci et al., 2009), and prior to exposure
to cannabis, higher in boys who later develop cannabis use disorder compared to those who used cannabis but did not develop
this disorder (Kirisci et al., 2009). It is noteworthy that no shared
environment component was detected for TLI (Vanyukov et al.,
2009), a finding that is consistent with studies showing that liability to drug use initiation is affected by shared and non-shared
environmental influences, whereas liability to addiction is largely
influenced by genetic factors (Kendler et al., 2000) and phenotype–
(and genotype-) environment correlation (i.e., when individuals
with certain characteristics are more likely to seek, and be found
in, environments conducive to accessing and using drugs) (e.g.,
Kirillova et al., 2008). These findings are part of the cumulative
science on the biobehavioral foundations of addiction. They reinforce the plausibility of mapping the propensity for and severity of
addiction along a common dimension of liability, in the Falconer
sense, i.e., extending across a graded scale whose degrees signify
gradations between sub- and suprathreshold liability phenotypes,
respectively.
Genes found to be associated with addiction-related characteristics, both in candidate gene and whole genome scan studies
(Uhl et al., 2008), are not specific to a particular drug, or even
to addictions as such, given their involvement in basic neurological mechanisms and signal transduction in the central nervous
system. It is thus likely that these associations are mediated by
pleiotropic nonspecific effects of these genes on neurobiological
processes involved in behavior regulation and control. This hypothesis is supported, for instance, by significant genetic correlations
between an index of behavior disinhibition (locating symptoms of
conduct and attention-deficit hyperactivity disorder, novelty seeking, and substance use on the same dimension) and an index of
performance on neuropsychological tasks measuring attentional
control and response inhibition (Young et al., 2009). A similar brainbehavior connection is noted for disinhibition/externalizing and
the reduced amplitude of the P300 event-related potential (Iacono
et al., 2002; Justus et al., 2001), particularly its main time–frequency
components, theta and delta, indexing, respectively, memory
encoding and attention processes, and signal matching, decision
making, and memory updating processes (Gilmore et al., 2009).
The association between P300 amplitude and an index of the
latent externalizing trait (accounting for variance shared between
liabilities to alcohol, nicotine and drug dependence, conduct disorder and adult antisocial behavior) is of genetic origin (Hicks
et al., 2007). As shown by genetic studies of phenotypes based
on neuroimaging, genetic associations of behavioral response regulation and personality characteristics comprising the behavior
dysregulation construct are mediated by the function of neural circuitry involving targets of drug action (Hariri et al., 2008; Hariri,
2009).
Statistically, substance use disorders and related symptoms
have been shown to best fit a model whereby their covariances
are substantially accounted for by a single dominant factor (Kirisci
et al., 2002, 2006; Compton et al., 2009; Saha et al., 2007; Wu et
al., 2009). Unidimensionality was also established for items that
comprise the TLI, the instrument (described above) that measures
transmissible liability to illicit drug-related substance use disorder in children. Interestingly, the TLI items are largely related
to a set of externalizing problems (e.g., conduct disorder, antisocial personality disorder) and temperament-like indicators of
nonspecific addiction risk (e.g., behavioral disinhibition, constraint,
sensation seeking, and novelty seeking) collectively referred to
as behavioral undercontrol, dysregulation, or externalizing psychopathology (Cadoret et al., 1995; Chassin et al., 1991; Conway et
al., 2002, 2003; Elkins et al., 2004; Iacono et al., 1999, 2008; Krueger
et al., 2002, 2007; Sher et al., 2000). Evidence suggests a common
genetic variance between these traits and addiction (Mustanski et
al., 2003). Some studies suggest that the broad externalizing factor
is more heritable than the constituent individual disorders (80–85%
versus 40–70%) (Hicks et al., 2004; Moffitt, 2005; Young et al., 2000),
as well as being more useful for gene identification (Dick et al.,
2008).
Variation in common (nonspecific) liability to addiction could
help explain the typical pattern of progression “softer” to “harder”
types of substances used (Tarter et al., 2006). Variation in externalizing traits could also mediate heritability of liability to addiction
(Button et al., 2006; Kirillova et al., 2008; Slutske et al., 2002;
Swendsen et al., 2002; Tarter et al., 2004; Vanyukov et al., 2007).
From a genetic-risk perspective, familial antisocial addiction may
be particularly potent (or severe). Studies of adopted-away offspring of fathers who are antisocial addicts (compared to either
antisocial or addicted), for example, have found the offspring to
be at greatest risk for substance abuse themselves (Langbehn et
al., 2003). Prevention research has demonstrated how interventions that directly or indirectly target externalizing problems can
effectively prevent drug abuse (Hawkins et al., 2008; Kellam et al.,
2008; Poduska et al., 2008), perhaps through impacting underlying
neuroregulation systems (Romer and Walker, 2007) and familial
processes that enhance behavioral regulation (Brody et al., 2009).
2. Severity measurement instruments
The following briefly reviews instruments that measure addiction propensity and severity (see Table 1). The instruments in
this review were identified through a literature search of sev-
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K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12
Table 1
Instruments designed to measure addiction severity.
Instrument
What it measures
Operationalization of severity
Item-selection methodology
Addiction Severity Instrument (ASI)
Severity of alcohol and
drug use
Severity of alcohol
dependence
Need for treatment across 6 domains
Clinical judgment
DSM symptomatology, loss of control,
obsessive drinking style, two aspects of
withdrawal
DSM symptomatology, pleasurable effects
of drug, perceived need for drug in order
to function properly
DSM symptomatology
Clinical judgment, item/factor
correlation
Consequences of drug use
Clinical judgment
DSM symptomatology, substance use
frequency, behavioral complexity
DSM symptomatology, three aspects of
withdrawal, rapidity of reinstatement
after abstinence
DSM symptomatology, three aspects of
withdrawal, rapidity of reinstatement
after abstinence, depression, lethargy
DSM symptomatology, compulsivity of
drug use
DSM symptomatology, three aspects of
withdrawal, rapidity of reinstatement
after abstinence
DSM symptomatology
Clinical judgment
DSM symptomatology, liability of
substance use
Clinical judgment, item/factor
correlation, item-response theory
Alcohol Dependence Scale (ADS)
Benzodiazepine Questionnaire (BDEQ)
Severity of benzodiazepine
dependence
Chemical Use, Abuse, and Dependence
Scale (CUAD)
Drug Use Screening Inventory (DUSI)
Global Appraisal of Individual Needs
(GAIN)
Severity of Alcohol Dependence
Questionnaire (SADQ)
Severity of alcohol and
drug use
Severity of alcohol and
drug use
Severity of alcohol and
drug use
Severity of alcohol
dependence
Severity of Amphetamine Dependence
Questionnaire (SamDQ)
Severity of amphetamine
dependence
Severity of Dependence Scale (SDS)
Severity of Opiate Dependence
Questionnaire (SODQ)
Severity of drug
dependence
Severity of opiate
dependence
Substance Dependence Severity Scale
(SDSS)
Substance Use Involvement Index
(SUII)
Severity of drug
dependence
Severity of alcohol and
drug use
eral databases (e.g., PubMed) for key terms such as “drug abuse
severity”, “drug addiction severity”, “substance abuse severity”,
“substance addiction severity”. To be included, an instrument had
to be explicitly designed to measure severity of drug or alcohol addiction. Excluded were instruments that measure nicotine
addiction,1 screening instruments, and those that measure only one
specific domain of addiction (e.g., craving). The review includes the
purpose and content of each instrument and other information on
the applicability of the instrument for liability measurement.
2.1. Addiction Severity Index (ASI)
The ASI (McLellan et al., 1980) is a widely used instrument that
assesses the need for alcohol or drug treatment across six domains
of functioning (Chemical Abuse, Medical, Psychological, Legal, Family/Social, and Employment/Support). Within each domain, the
interviewer provides global ratings of severity (referred to as Interviewer Severity Ratings, or ISRs) based on the patient’s responses
to objective items as well as the patient’s assessment of how bothered he/she is by problems in each domain. The ISRs, which range
from 0 to 9, form the basis for the patient’s profile and treatment plan. Revised versions of the ASI (McDermott et al., 1996)
use more advanced psychometric methods, partially in response
to studies that failed to replicate high inter-rater reliabilities of
the original version of the ASI (McLellan et al., 1985; Hodgins and
Elguebaly, 1992). The ASI is one of the most frequently used instruments in addiction research and practice, and it serves as one of
the core measures in the NIDA Clinical Trials Network. The reliability and validity of the ASI is well documented, but less is known
about its dimensionality or whether its items reflect differences in
severity.
1
Considerable research attention has been focused on the severity of nicotine
addiction, and results suggest a latent trait for nicotine dependence (Strong et al.,
2003a,b, 2007, 2009). Less is known about the severity of alcohol and/or illicit drug
addiction, however, which underscores the need for additional research of this kind.
Clinical judgment, item/factor
correlation
Clinical judgment
Clinical judgment, item/factor
correlation
Clinical judgment, item/factor
correlation
Clinical judgment, item/factor
correlation
Clinical judgment, item/factor
correlation
Clinical judgment
2.2. Drug Use Screening Inventory (DUSI)
The DUSI (Kirisci et al., 1995; Tarter et al., 1990) was developed as a diagnostic instrument for the treatment of alcohol or
drug problems in adolescents. Despite the word “screening” in its
name, this is a phenotypic assessment instrument comprising a
series of scales measuring severity of substance abuse as reflected
in various life domains: Substance Use, Behavior Patterns, Health
Status, Psychiatric Disorder, Social Skills, Family System, School
Adjustment, Work, Peer Relationships, Leisure/Recreation. Kirisci
et al. (1995) found that the items are scalable as indicators of
a unidimensional latent trait, that the scores among individuals
with high or moderate substance use were measured with greater
precision than those at lower levels, and that scores accurately predicted the level of substance use and consequences at 6-month
follow-up.
2.3. Global Appraisal of Individual Needs (GAIN)
The GAIN (Dennis et al., 2003) is a large set of questionnaires
designed to provide diagnostic and severity information for both
adolescents and adults seeking treatment for drug or alcohol problems. The GAIN’s 16-item Substance Problem Scale (SPS) scale
consists of the DSM-IV criteria for substance use diagnosis plus five
screener items that measure variation in severity (i.e., weekly use,
family and friends complaining about use, continued use despite
fights, and use being time consuming). Acceptable reliability and
validity estimates have been reported (1999 manual and Dennis et
al., 2006). Riley et al. (2007) found that the five screener items had
relatively low location parameters, indicating that the items reflect
variation at the lower (or “milder”) end of a severity continuum.
2.4. Severity of Alcohol Dependence Questionnaire (SADQ) and
Alcohol Dependence Scale (ADS)
The SADQ (Stockwell et al., 1979) and the ADS (Skinner and
Allen, 1982) were explicitly designed to extend the Edwards and
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Gross (1976) conceptualization of alcoholism to severity of addiction to illicit drugs. The SADQ includes four subscales: physical
withdrawal, affective withdrawal, drinking to relieve withdrawal,
and rapidity of reinstatement after abstinence. The ADS includes
four slightly broader subscales: psychophysical withdrawal, psychoperceptual withdrawal, loss of behavioral control, and obsessive
drinking style. Reliability and validity estimates were high for both
instruments. Kahler et al. (2003a,b) reported that the ADS items
primarily measure a single dimension of alcoholism severity, with
some items corresponding to greater severity than others.
2.5. Severity of Opioid Dependence Questionnaire (SODQ), the
Severity of Amphetamine Questionnaire (SamDQ), and the
Benzodiazepine Dependence Questionnaire (BDEPQ)
The SODQ and the SamDQ adapted the SADQ for opioids and
amphetamines, respectively. These instruments consist of subscales designed to measure the severity of physical withdrawal
symptoms, affective withdrawal symptoms, the extent to which
drugs are used to relieve withdrawal symptoms, and the rapidity of reinstatement after periods of abstinence. The BDEPQ (Baillie
and Mattick, 1996) assesses aspects of benzodiazepine dependence
including the extent to which pleasurable effects were anticipated
as a result of drug use, the extent to which drug use is needed to
complete daily life activities, and a general dependence factor. The
reliability and validity of these instruments are well documented
(Baillie and Mattick, 1996; Churchill et al., 1993; Sutherland et al.,
1986; Topp and Mattick, 1997), but information is limited regarding dimensionality and whether some items reflect greater severity
than others.
2.6. Chemical Use Abuse and Dependence (CUAD)
The CUAD scale (Mcgovern and Morrison, 1992) is a semistructured interview designed to provide DSM-III-R substance use
diagnoses as well as severity indicators in both clinical and research
settings. For each substance used, weights based on clinical judgment are assigned for each symptom to reflect increasing clinical
severity, and total severity scores are computed by summing the
weights across symptoms. The CUAD has demonstrated reliability and validity (Mcgovern and Morrison, 1992), but it is unknown
whether it is unidimensional or whether some items reflect greater
severity than others.
2.7. Substance Dependence Severity Scale (SDSS)
The SDSS (Miele et al., 2000) is a semi-structured interview
designed to provide DSM-IV diagnoses and severity indicators in
both clinical and research settings. For each substance used, the
SDSS provides DSM-IV diagnoses and four different 30-day severity
indicators; two for intensity of symptoms (SEV, WORST SEV), and
two for frequency of symptoms (DAYS, WORST DAYS). The reliability and validity of the SDSS is documented, but information is
lacking about its dimensionality and ability to assess severity.
2.8. Severity of Dependence Scale (SDS)
The SDS (Gossop et al., 1995) was developed as a brief scale to
measure the degree of dependence experienced by different types
of drug users. In contrast to the other instruments discussed here,
the five-question SDS “is primarily a measure of compulsive use
and it does not include items to measure tolerance, withdrawal, or
reinstatement” (p. 612). Responses are self rated on a 4-point Likert
scale, and summed to form a total score. Scale reliability and validity has been reported and factor analytic methods have confirmed
unidimensionality of the five items.
2.9. Substance Use Involvement Index (SUII)
The Substance Use Involvement Index (SUII; Kirisci et al., 2002),
derived using item-response theory (IRT; described below), is
based on the respondent’s endorsement of lifetime use (yes/no)
of 10 categories of drugs: alcohol, cannabis, cocaine/crack, opiates,
amphetamines, methylphenidate, sedatives, tobacco, hallucinogens, PCP and inhalants. One study reported that a unidimensional
factor model adequately fit the data for both adult males and
adult females, and the factor loadings were not significantly different between gender groups (Kirisci et al., 2002). Interestingly,
Kirisci and colleagues also reported that equated item-location
parameters were higher in females than males, suggesting that
endorsement of substance use by a male is (by itself) indicative
of a lower level of severity than the same endorsement by a female.
These results are consistent with data showing that females have
a higher liability threshold (Kendler et al., 2003b), i.e., they require
higher factor scores in order to manifest a disorder.
3. Propensity measurement instruments
Measurement of propensity to addiction has rarely been
attempted and is considerably more difficult than measurement
of addiction severity, especially given the absence of face-value
indicators of risk with high construct validity. Obviously, only
premorbid characteristics may be safely used as indicators of
propensity because drug use and addiction have behavioral and
psychological effects. Despite knowledge of many “risk” and “protection” factors (psychological and psychopathological variables
associated with addiction risk) (Glantz et al., 2005; Hawkins et al.,
1992), it is not evident on an a priori basis that premorbid indicators can be used, or how, in constructing an index of propensity to
addiction.
3.1. Transmissible Liability Index (TLI)
One approach to the selection of propensity indicators has been
used in the derivation of the index of transmissible liability to addiction related to illicit drugs (the Transmission Liability Index, TLI),
based on a high-risk/family design and IRT in the NIDA-funded
Center for Education and Drug Abuse Research (CEDAR). Inasmuch
as addiction risk is transmissible within families (mostly due to
its high heritability), characteristics that discriminate groups of
children with affected versus unaffected parents (high- and lowaverage-risk groups, HAR and LAR, respectively) are likely to be
indicators of the transmissible component of children’s addiction
liability/propensity (the variance component correlated between
relatives/generations).
The TLI derivation method involves using a large set of items
from numerous psychological and psychiatric instruments that
were originally selected based on their potential for measuring
variables related to addiction risk and psychopathology. Through
a variety of statistical methodologies (e.g., factor analysis, IRT),
the items were selected and transformed into a set of unidimensional constructs characterizing individual behavior/personality
(e.g., antisociality, attention, mood) (see measurement model and
other details in Vanyukov et al., 2003a). The resulting 45-item set
selected for sons of the probands at age 10–12 was used to assess
the quality of items and estimate TLI. The findings show that the TLI
is a valid and reliable scale, and highly predictive (e.g., O.R. = 1.81,
95% C.I.: 1.12–2.30) of substance use disorder (Vanyukov et al.,
2009; Kirisci et al., 2009). Twin studies have also shown that TLI
is highly heritable (H2 = 0.79) (Vanyukov et al., 2009; Hicks et al.,
in press), further supporting its derivation as a measure of a transmissible trait.
K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12
The longitudinal design of the CEDAR study permits further
refinement of the method used in the creation of the TLI, such that
the liability/propensity indicator constructs and items will be referenced to the offspring’s own, rather than parental, outcomes such
as drug use and drug addiction. Such referencing, aside from being
based entirely on the offspring’s own phenotype, directly connects
indicators and a given manifest trait, which will enable indexing
of the resultant total, rather than only transmissible, liability. This
extended liability index methodology has potential as a prototype
for development of an index to cover the entire range of phenotypic
distribution (i.e., both propensity and severity) and identify candidate biologic and genetic mechanisms of drug use and its precursors
and antecedents.
4. Summary and future directions
4.1. Limitations of existing instruments
The assessment instruments in use today for measuring addiction severity (described above and appearing in Table 1) have
proven utility, reliability, and validity, but they are of limited use
for evaluating individual differences in propensity/severity of the
hallmark characteristic(s) of drug addiction, i.e., compulsivity in
seeking and using drugs despite harmful consequences. First, existing instruments are based on a variety of related but different
constructs of addiction severity including behavioral and social
consequences, quantity or type of DSM symptoms endorsed, use
patterns within and across substances, and number of different
DSM diagnoses. Only one, the SDS, focuses on compulsive drug
taking and seeking. Second, the content of existing measures is
unlikely to reflect the full range of addiction severity. This limitation can be partly attributed to the fact that the content of many
instruments derives from the DSM, which virtually by design, is
not optimal for measuring variability in the severity of addiction.
Indeed, clinical and epidemiologic studies applying IRT models
show that DSM symptoms are redundant and fail to capture variability across the full range of the addiction continuum (Compton et
al., 2009; Gillespie et al., 2007; Langenbucher et al., 1995; Saha et al.,
2007; Wu et al., 2009). Many existing instruments also rely on clinical judgment in the absence of item-selection techniques, thereby
increasing the likelihood of including items that are redundant,
collinear, or weakly related to the construct of interest.
Third, only one extant instrument (TLI) is currently available to
measure propensity to addiction, let alone both the propensity and
the severity of addiction on the same metric. It should be noted
that the latent liability phenotype assessed by the TLI, while pertaining to the risk in the nonaffected individuals and thereby to
prospective prediction, is nevertheless a cross-sectional measure.
Substance use or lack thereof, or consumption with multiple graded
categories (e.g., none, low, heavy) are observed phenotypic outcomes that may or may not be used as indicators of latent liability.
Using items immediately related to drug use may be problematic,
of course, because that would preclude the index’s use in studying respective behaviors as outcomes. Although substance use is
a necessary condition for addiction development, it seems likely
that the psychological/behavioral components of liability such as
personality largely influence substance use initiation and/or, more
importantly, development of addiction. Substance use can thus be
viewed as a manifestation of liability forming an ontogenetic trajectory that can be tracked and measured.
Fourth, most extant instruments were constructed using classical test theory methodology, which has its own psychometric
limitations that directly bear on severity measurement. The use of
factor analysis, for example, does not utilize the full power of modern scaling techniques (Embretson and Reise, 2000; Hambleton
9
and Swaminathan, 1985; Lord and Novick, 1968) that take item
properties into account (e.g., whether one item indicates greater
“severity” than another item). While producing scales with high
levels of internal consistency, the use of factor analytic methods
alone does not allow one to build instruments to discriminate individuals along selected ranges of an underlying trait.
4.2. Advantages of item-response theory (IRT) for measuring
propensity and severity
Whereas various methods are available to analyze latent traits,
IRT appears particularly suited for the derivation of an index of liability to addiction. Briefly, IRT (e.g., Embretson and Reise, 2000) is a
psychometric test theory that relates the performance of an examinee on a test item to a latent trait (ability) that the test is intended to
measure. This relationship (e.g., in a simple case, between the trait
and the probability of a correct response) is described by an itemresponse function (IRF). While ability level is a characteristic of the
examinee, the examinee’s performance will also depend on parameters that characterize the test items themselves. The widely used
two-parameter model includes location (difficulty) or b, the trait
value at which the probability of a correct response exceeds 0.5, and
discrimination or a, which is proportional to the slope of the IRF at
point b on the trait scale. In this model, the parameters provide for
gradients in item difficulty and capacity to discriminate different
values of the trait. In contrast to the classical psychometric test theory, which was used to develop most of the instruments reviewed
herein, IRT provides testable models. A data-fitting IRT model yields
estimates that have unique value for trait measurement; i.e., item
parameters are invariant across samples (subpopulations) of subjects (the trait distribution does not influence the estimates), and
trait estimates are invariant across items used.
The many advantages of IRT have been underutilized in severity
instrument development, though it has successfully been used in
the measurement of transmissible addiction risk (Vanyukov et al.,
2003a,b, 2009; Kirisci et al., 2006, 2009). Selection of items based
on the information they contribute is a key advantage of itemresponse theory (Hambleton et al., 1991), which is likely to inform
the development of the liability index. The most difficult question
centers on the problem of selecting and constructing items that are
highly informative for measuring low propensity values. Nevertheless, some such items (e.g., “I don’t move around much at all in my
sleep”, “I usually eat the same amount each day”, and “Changes in
plans make my child restless”) have been identified and applied in
an existing propensity measurement instrument, the TLI (Kirisci et
al., 2009; Vanyukov et al., 2009). These and other items are potential candidates for an instrument that measures both propensity
and severity on the same scale.
How and to what extent the propensity and severity phenotypes
can be characterized by a common, underlying, and continuous unidimensional trait of liability to addiction (Chan et al., 2008) remain
large, empirical challenges suitable for IRT analyses. Dimensionsharing between propensity and severity need not be perfect for
the derivation of a unidimensional index to quantify sub- and
suprathreshold liability; showing that a unidimensional model
offers the best fit for the data is sufficient. The unidimensionality
of a trait essentially refers to the structure of covariance between
items used in its measurement (and testable for their dominance
in accounting for the covariance) rather than to a single causal
influence or the total number of influences potentially determining the shared variance. Even if there is specificity to the unaffected
and affected phenotypic distributions (i.e., those of propensity and
severity), the variance they share with the common liability dimension, based on genetic data, will likely be large and sufficiently
informative to be targeted for measurement. If such tests fail and
the two constructs do not share the same dimension to an appre-
10
K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12
ciable degree, continuous indices will need to be developed that
quantitate these constructs separately and are applied to the nonaffected (or asymptomatic) and affected (symptomatic) populations.
Measurement of common rather than drug-specific liability is also
desirable from a practical standpoint, given its universal application.
4.3. Advantages of an instrument measuring propensity to and
severity of addiction
The conceptual and methodological shortcomings of instruments currently used in research and clinical practice can be
overcome through the use of emerging technologies to develop a
reliable, valid, and standardized assessment instrument to measure and distinguish variations in phenotypic expression of the
underlying latent trait that comprises propensity and severity of
drug addiction. The distribution of the hypothesized latent trait
in the population is depicted in Fig. 1. Key requirements of this
assessment instrument include the ability to accurately and comprehensively measure gradations along the propensity/severity
continuum using broad and discriminating content that captures
the essence of addiction; to detect meaningful variation between,
within, and across individuals over time that is scalable along the
underlying dimension; and to allow for efficient assessment of the
construct with minimal burden for administration, training, and
cost to the researcher, clinician, research participant, or patient.
Coupling modern psychometric methodology with sophisticated
computer technology (e.g., IRT-based computerized adaptive testing [CAT]) enables measurement covering the full phenotypic scale,
with maximum flexibility, accuracy, and efficiency (Drasgow and
Olson-Buchanan, 1999; Kirisci and Hsu, 1992). CAT-based instruments, for example, can administer different sets of items to
different people optimal for their liability phenotypes, can provide
analyses to explore the relative difficulty of items, and can generate a single score to represent an individual’s location along an
underlying trait (the liability phenotype). This technique is routinely applied in the education field (e.g., the administration of
the Graduate Record Examination applies IRT analyses), and can
certainly be adapted for use in the field of addiction.
Whether propensity to and severity of addiction are locatable on
essentially the same dimension remains to be determined, yet the
cumulative evidence on neurobiological mechanisms of addiction,
combined with psychometric evidence, gives merit to a common
metric of liability to addiction extending across subclinical to clinical diagnostic thresholds. The concept of addiction liability as an
unidimensional trait that is complex, i.e., contributed to by many
diverse influences, and dynamic is evidenced from a number of
other complex, multifactorial traits, such as stature (an observed
trait) or the Intelligence Quotient (IQ, an index of a latent trait). Such
an instrument, as a continuous measure of addiction propensity
and severity at the behavioral level, would enhance the capacity,
statistical power, and precision of research on the etiology, neurobiology, and genetics of addiction (Embretson and Reise, 2000;
Hambleton and Swaminathan, 1985; Lord and Novick, 1968; Neale
et al., 2006), and for use in developing improved approaches in
primary prevention, treatment, and intervention. For instance, individuals with high severity scores may require different types of
treatments than those with lower scores, and individuals with
elevated propensity scores may require preventive interventions
tailored to their particular needs. In addition, severity measures
have been rarely applied to identify neurobiological correlates of
addiction. Volkow et al. (2006) found that cue-induced dopamine
changes in cocaine-dependent subjects positively correlated with
the average ratings on the seven domains of the ASI. Patkar et al.
(2008) reported that blunted responses to a serotonergic challenge
among cocaine-dependent subjects positively correlated with the
drug subscale scores of the ASI. Clearly, research of this kind can
be enhanced by improved measurement of individual differences
in the propensity and severity of addiction. Finally, from a translational perspective, such an instrument would unify and potentially
transform research on the addiction liability phenotype by using a
single instrument. The field of addiction science has advanced in
remarkable ways from discoveries in psychometrics, computerization, etiology, neurobiology, genetics, prevention, and treatment.
The challenge before us now is to build on these advances to
develop a new instrument that measures the propensity and severity of addiction.
Conflict of interest
Author Swan served on the Pfizer National Advisory Board (1
day, 2008) to consult on issues related to varenicline.
Acknowledgements
Role of funding source: NIDA had no role in the analysis, preparation, and writing of the report, or in the decision to submit the
paper for publication.
Contributors: Author Conway took primary responsibility for
writing the manuscript drafts. Authors Conway and Levy managed
the literature searches and summaries of related work. Authors
Levy, Vanyukov, Chandler, Rutter, Swan, and Neale contributed to
the manuscript design, writing, and editing of manuscript drafts.
All authors contributed to and have approved the final manuscript.
References
Adinoff, B., 2004. Neurobiologic processes in drug reward and addiction. Harv. Rev.
Psychiatry 12, 305–320.
Baillie, A.J., Mattick, R.P., 1996. The benzodiazepine dependence questionnaire:
development, reliability and validity. Br. J. Psychiatry 169, 276–281.
Bierut, L.J., Dinwiddie, S.H., Begleiter, H., Crowe, R.R., Hesselbrock, V., Nurnberger Jr.,
J.I., Porjesz, B., Schuckit, M.A., Reich, T., 1998. Familial transmission of substance
dependence: alcohol, marijuana, cocaine, and habitual smoking: a report from
the Collaborative Study on the Genetics of Alcoholism. Arch. Gen. Psychiatry 55,
982–988.
Brody, G.H., Beach, S.R., Philibert, R.A., Chen, Y.F., Lei, M.K., Murry, V.M., Brown,
A.C., 2009. Parenting moderates a genetic vulnerability factor in longitudinal
increases in youths’ substance use. J. Consult. Clin. Psychol. 77, 1–11.
Button, T.M., Hewitt, J.K., Rhee, S.H., Young, S.E., Corley, R.P., Stallings, M.C., 2006.
Examination of the causes of covariation between conduct disorder symptoms
and vulnerability to drug dependence. Twin Res. Hum. Genet. 9, 38–45.
Cadoret, R.J., Yates, W.R., Troughton, E., Woodworth, G., Stewart, M.A., 1995. Adoption study demonstrating two genetic pathways to drug abuse. Arch. Gen.
Psychiatry 52, 42–52.
Chan, Y.F., Dennis, M.L., Funk, R.R., 2008. Prevalence and comorbidity of major internalizing and externalizing problems among adolescents and adults presenting
to substance abuse treatment. J. Subst. Abuse Treat. 34, 14–24.
Chassin, L., Rogosch, F., Barrera, M., 1991. Substance use and symptomatology among
adolescent children of alcoholics. J. Abnorm. Psychol. 100, 449–463.
Churchill, A.C., Burgess, P.M., Pead, J., Gill, T., 1993. Measurement of the severity of
amphetamine dependence. Addiction 88, 1335–1340.
Compton, W.M., Saha, T.D., Conway, K.P., Grant, B.F., 2009. The role of cannabis use
within a dimensional approach to cannabis use disorders. Drug Alcohol Depend.
100, 221–227.
Conway, K.P., Kane, R.J., Ball, S.A., Poling, J.C., Rounsaville, B.J., 2003. Personality, substance of choice, and polysubstance involvement among substance dependent
patients. Drug Alcohol Depend. 71, 65–75.
Conway, K.P., Swendsen, J.D., Rounsaville, B.J., Merikangas, K.R., 2002. Personality,
drug of choice, and comorbid psychopathology among substance abusers. Drug
Alcohol Depend. 65, 225–234.
Conway, K.P., Compton, W.M., Miller, P.M., 2006. Editorial: Novel approaches to
phenotyping drug abuse. Addict. Behav. 31, 923–928.
Craddock, N., O’Donovan, M.C., Owen, M.J., 2008. Genome-wide association studies in psychiatry: lessons from early studies of non-psychiatric and psychiatric
phenotypes. Mol. Psychiatry 13, 649–653.
Dawson, D.A., Grant, B.F., Stinson, F.S., Chou, P.S., 2006. Maturing out of alcohol
dependence: the impact of transitional life events. J. Stud. Alcohol 67, 195–203.
Dennis, M., Scott, C.K., 2007. Managing addiction as a chronic condition. Addict. Sci.
Clin. Pract. 4, 45–55.
Dennis, M., Titus, J., Unsicker, J., Hodkgins, D., 2003. Global Appraisal of Individual Needs: Administration Guide for the GAIN and Related Measures. Chestnut
Health Systems, Bloomington, IL.
K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12
Dennis, M.L., Chan, Y.F., Funk, R.R., 2006. Development and validation of the GAIN
short screener (GSS) for internalizing, externalizing and substance use disorders
and crime/violence problems among adolescents and adults. Am. J. Addict. 15,
80–91.
Dennis, M.L., Scott, C.K., Funk, R., Foss, M.A., 2005. The duration and correlates of
addiction and treatment careers. J. Subst. Abuse Treat. 28 (Suppl. 1), S51–S62.
Dick, D.M., Aliev, F., Wang, J.C., Grucza, R.A., Schuckit, M., Kuperman, S., Kramer,
J., Hinrichs, A., Bertelsen, S., Budde, J.P., Hesselbrock, V., Porjesz, B., Edenberg,
H.J., Bierut, L.J., Goate, A., 2008. Using dimensional models of externalizing psychopathology to aid in gene identification. Arch. Gen. Psychiatry 65, 310–318.
Drasgow, F., Olson-Buchanan, J.B., 1999. Innovations in Computerized Assessment.
Lawrence Erlbaum Associates, Mahwah.
Edwards, G., Gross, M.M., 1976. Alcohol dependence—provisional description of a
clinical syndrome. Br. Med. J. 1, 1058–1061.
Elkins, I.J., McGue, M., Malone, S., Iacono, W.G., 2004. The effect of parental alcohol
and drug disorders on adolescent personality. Am. J. Psychiatry 161, 670–676.
Embretson, S.E., Reise, W.S.P., 2000. Item Response Theory for Psychologists.
Lawrence Erlbaum Associates, Mahwah, NJ.
Falconer, D.S., 1965. The inheritance of liability to certain diseases, estimate from
the incidence among relatives. Ann. Hum. Genet. 29, 51–71.
Foll, B.L., Gallo, A., Le, S.Y., Lu, L., Gorwood, P., 2009. Genetics of dopamine receptors
and drug addiction: a comprehensive review. Behav. Pharmacol. 20, 1–17.
Gillespie, N.A., Neale, M.C., Prescott, C.A., Aggen, S.H., Kendler, K.S., 2007. Factor
and item-response analysis DSM-IV criteria for abuse of and dependence on
cannabis, cocaine, hallucinogens, sedatives, stimulants and opioids. Addiction
102, 920–930.
Gilmore, C.S., Malone, S.M., Bernat, E.M., Iacono, W.G., 2009. Relationship between
the P3 event-related potential, its associated time–frequency components, and
externalizing psychopathology. Psychophysiology.
Glantz, M.D., Conway, K.P., Colliver, J.D., 2005. Drug abuse heterogeneity and the
search for subtypes. In: Sloboda, Z. (Ed.), Epidemiology of Drug Abuse. Kluwer
Academic/Plenum Publishers, New York, pp. 15–28.
Goldstein, R.Z., Volkow, N.D., 2002. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am.
J. Psychiatry 159, 1642–1652.
Gossop, M., Darke, S., Griffiths, P., Hando, J., Powis, B., Hall, W., Strang, J., 1995.
The Severity of Dependence Scale (Sds)—psychometric properties of the Sds
in English and Austrian samples of heroin, cocaine and amphetamine users.
Addiction 90, 607–614.
Hambleton, R.K., Swaminathan, H.S., 1985. Item Response Theory Principles and
Applications. Kluwer Nijhoff Publishing, Boston.
Hambleton, R.K., Swaminathan, H., Rogers, H.J., 1991. Fundamentals of item response
theory. Sage, Newbury Park, CA.
Hariri, A.R., 2009. The neurobiology of individual differences in complex behavioral
traits. Annu. Rev. Neurosci. 32, 225–247.
Hariri, A.R., Gorka, A., Hyde, L.W., Kimak, M., Halder, I., Ducci, F., Ferrell, R.E.,
Goldman, D., Manuck, S.B., 2008. Divergent effects of genetic variation in endocannabinoid signaling on human threat- and reward-related brain function. Biol.
Psychiatry.
Hawkins, J.D., Catalano, R.F., Miller, J.Y., 1992. Risk and protective factors for alcohol
and other drug problems in adolescence and early adulthood—implications for
substance-abuse prevention. Psychol. Bull. 112, 64–105.
Hawkins, J.D., Kosterman, R., Catalano, R.F., Hill, K.G., Abbott, R.D., 2008. Effects
of social development intervention in childhood 15 years later. Arch. Pediatr.
Adolesc. Med. 162, 1133–1141.
Hicks, B.M., Bernat, E., Malone, S.M., Iacono, W.G., Patrick, C.J., Krueger, R.F., McGue,
M., 2007. Genes mediate the association between P3 amplitude and externalizing disorders. Psychophysiology 44, 98–105.
Hicks, B.M., Iacono, W.G., McGue, M., in press. Index of the transmissible common
liability to addiction: heritability and prospective associations with substance
abuse and related outcomes. Addiction.
Hicks, B.M., Krueger, R.F., Iacono, W.G., McGue, M., Patrick, C.J., 2004. Family transmission and heritability of externalizing disorders: a twin-family study. Arch.
Gen. Psychiatry 61, 922–928.
Hodgins, D.C., Elguebaly, N., 1992. More data on the addiction severity
index—reliability and validity with the mentally-Ill substance-abuser. J. Nerv.
Ment. Dis. 180, 197–201.
Hyman, S.E., Malenka, R.C., Nestler, E.J., 2006. Neural mechanisms of addiction: the
role of reward-related learning and memory. Annu. Rev. Neurosci. 29, 565–598.
Iacono, W.G., Carlson, S.R., Malone, S.M., McGue, M., 2002. P3 event-related potential
amplitude and the risk for disinhibitory disorders in adolescent boys. Arch. Gen.
Psychiatry 59, 750–757.
Iacono, W.G., Carlson, S.R., Taylor, J., Elkins, I.J., McGue, M., 1999. Behavioral disinhibition and the development of substance-use disorders: findings from the
Minnesota Twin Family Study. Dev. Psychopathol. 11, 869–900.
Iacono, W.G., Malone, S.M., McGue, M., 2008. Behavioral disinhibition and the development of early-onset addiction: common and specific influences. Annu. Rev.
Clin. Psychol. 4, 325–348.
Justus, A.N., Finn, P.R., Steinmetz, J.E., 2001. P300, disinhibited personality, and earlyonset alcohol problems. Alcohol. Clin. Exp. Res. 25, 1457–1466.
Kahler, C.W., Strong, D.R., Hayaki, J., Ramsey, S.E., Brown, R.A., 2003a. An item
response analysis of the alcohol dependence scale in treatment-seeking alcoholics. J. Stud. Alcohol 64, 127–136.
Kahler, C.W., Strong, D.R., Stuart, G.L., Moore, T.M., Ramsey, S.E., 2003b. Item functioning of the alcohol dependence scale in a high-risk sample. Drug Alcohol
Depend. 72, 183–192.
11
Kalivas, P.W., O’Brien, C., 2007. Drug addiction as a pathology of staged neuroplasticity. Neuropsychopharmacology 33, 166–180.
Kalivas, P.W., Volkow, N.D., 2005. The neural basis of addiction: a pathology of
motivation and choice. Am. J. Psychiatry 162, 1403–1413.
Kellam, S.G., Brown, C.H., Poduska, J.M., Ialongo, N.S., Wang, W., Toyinbo, P., Petras,
H., Ford, C., Windham, A., Wilcox, H.C., 2008. Effects of a universal classroom
behavior management program in first and second grades on young adult behavioral, psychiatric, and social outcomes. Drug Alcohol Depend. 95 (Suppl. 1),
S5–S28.
Kendler, K.S., Karkowski, L.M., Neale, M.C., Prescott, C.A., 2000. Illicit psychoactive
substance use, heavy use, abuse, and dependence in a US population-based
sample of male twins. Arch. Gen. Psychiatry 57, 261–269.
Kendler, K.S., Jacobson, K.C., Prescott, C.A., Neale, M.C., 2003a. Specificity of genetic
and environmental risk factors for use and abuse/dependence of cannabis,
cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. Am.
J. Psychiatry 160, 687–695.
Kendler, K.S., Myers, J., Prescott, C.A., 2007. Specificity of genetic and environmental
risk factors for symptoms of cannabis, cocaine, alcohol, caffeine, and nicotine
dependence. Arch. Gen. Psychiatry 64, 1313–1320.
Kendler, K.S., Prescott, C.A., Myers, J., Neale, M.C., 2003b. The structure of genetic and
environmental risk factors for common psychiatric and substance use disorders
in men and women. Arch. Gen. Psychiatry 60, 929–937.
Kirillova, G.P., Vanyukov, M.M., Kirisci, L., Reynolds, M., 2008. Physical maturation,
peer environment, and the ontogenesis of substance use disorders. Psychiatry
Res. 158, 43–53.
Kirisci, L., Hsu, T., 1992. Ability Estimation Using Only Observable Quantities in
Adaptive Testing. ERIC Document Reproduction Service No. TM 018393.
Kirisci, L., Mezzich, A., Tarter, R., 1995. Norms and sensitivity of the adolescent version of the drug-use screening inventory. Addict. Behav. 20, 149–
157.
Kirisci, L., Tarter, R., Mezzich, A., Ridenour, T., Reynolds, M., Vanyukov, M., 2009.
Prediction of cannabis use disorder between boyhood and young adulthood
clarifying the phenotype and environtype. Am. J. Addict. 18, 36–47.
Kirisci, L., Tarter, R.E., Vanyukov, M., Martin, C., Mezzich, A., Brown, S., 2006. Application of item response theory to quantify substance use disorder severity. Addict.
Behav. 31, 1035–1049.
Kirisci, L., Vanyukov, M., Dunn, M., Tarter, R., 2002. Item response theory modeling
of substance use: an index based on 10 drug categories. Psychol. Addict. Behav.
16, 290–298.
Koob, G.F., Le, M.M., 1997. Drug abuse: hedonic homeostatic dysregulation. Science
278, 52–58.
Koob, G.F., Volkow, N.D., 2009. Neurocircuitry of addiction. Neuropsychopharmacology.
Kreek, M.J., Nielsen, D.A., Butelman, E.R., LaForge, K.S., 2005. Genetic influences on
impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and
addiction. Nat. Neurosci. 8, 1450–1457.
Krueger, R.F., Hicks, B.M., Patrick, C.J., Carlson, S.R., Iacono, W.G., McGue, M., 2002.
Etiologic connections among substance dependence, antisocial behavior, and
personality: modeling the externalizing spectrum. J. Abnorm. Psychol. 111,
411–424.
Krueger, R.F., Markon, K.E., Patrick, C.J., Benning, S.D., Kramer, M.D., 2007. Linking
antisocial behavior, substance use, and personality: an integrative quantitative
model of the adult externalizing spectrum. J. Abnorm. Psychol. 116, 645–666.
Langbehn, D.R., Cadoret, R.J., Caspers, K., Troughton, E.P., Yucuis, R., 2003. Genetic and
environmental risk factors for the onset of drug use and problems in adoptees.
Drug Alcohol Depend. 69, 151–167.
Langenbucher, J.W., Morgenstern, J., Miller, K.J., 1995. Dsm-Iii, Dsm-Iv and Icd-10 as
severity scales for drug-dependence. Drug Alcohol Depend. 39, 139–150.
Li, M.D., Burmeister, M., 2009. New insights into the genetics of addiction. Nat. Rev.
Genet. 10, 225–231.
Lord, F.M., Novick, M.R., 1968. Statistical Theories of Mental Test Scores. AddisonWesley Publishing Company, Reading, MA.
McDermott, P.A., Alterman, A.I., Brown, L., Zaballero, A., Snider, E.C., Mckay, J.R.,
1996. Construct refinement and confirmation for the addiction severity index.
Psychol. Assess. 8, 182–189.
Mcgovern, M.P., Morrison, D.H., 1992. The Chemical Use, Abuse, and Dependence Scale (Cuad)—rationale, reliability, and validity. J. Subst. Abuse Treat. 9,
27–38.
McLellan, A.T., Luborsky, L., Cacciola, J., Griffith, J., Evans, F., Barr, H.L., Obrien, C.P.,
1985. New data from the addiction severity index—reliability and validity in 3
centers. J. Nerv. Ment. Dis. 173, 412–423.
McLellan, A.T., Luborsky, L., Woody, G.E., O’Brien, C.P., 1980. An improved diagnostic evaluation instrument for substance abuse patients. The Addiction Severity
Index. J. Nerv. Ment. Dis. 168, 26–33.
Merikangas, K.R., Conway, K.P., 2009. Genetic epidemiology of substance use disorders. In: Charney, D.S., Nester, E.J. (Eds.), Neurobiology of Mental Illness. Oxford
University Press, New York, pp. 786–800.
Merikangas, K.R., Conway, K.P., 2008. Genetic epidemiology of substance use disorders. In: Charney, D.S., Nester, E.J. (Eds.), Neurobiology of Mental Illness, third.
Merikangas, K.R., Stolar, M., Stevens, D.E., Goulet, J., Preisig, M.A., Fenton, B., Zhang,
H., O’Malley, S.S., Rounsaville, B.J., 1998. Familial transmission of substance use
disorders. Arch. Gen. Psychiatry 55, 973–979.
Merikangas, K.R., Avenevoli, S., 2000. Implications of genetic epidemiology for the
prevention of substance use disorders. Addict. Behav. 25, 807–820.
Miele, G.M., Carpenter, K.M., Cockerham, M.S., Trautman, K.D., Blaine, J., Hasin, D.S.,
2000. Substance Dependence Severity Scale (SDSS): reliability and validity of
12
K.P. Conway et al. / Drug and Alcohol Dependence 111 (2010) 4–12
a clinician-administered interview for DSM-IV substance use disorders. Drug
Alcohol Depend. 59, 63–75.
Moffitt, T.E., 2005. Genetic and environmental influences on antisocial behaviors:
evidence from behavioral-genetic research. Adv. Genet. 55, 41–104.
Mustanski, B.S., Viken, R.J., Kaprio, J., Rose, R.J., 2003. Genetic influences on the association between personality risk factors and alcohol use and abuse. J. Abnorm.
Psychol. 112, 282–289.
Neale, M.C., Aggen, S.H., Maes, H.H., Kubarych, T.S., Schmitt, J.E., 2006. Methodological issues in the assessment of substance use phenotypes. Addict. Behav. 31,
1010–1034.
Newcomb, M.D., Galaif, E.R., Locke, T.F., 2001. Substance use diagnoses within a
community sample of adults: distinction, comorbidity, and progression over
time. Prof. Psychol.: Res. Pract. 32, 239–247.
O’Brien, C.P., 2003. Research advances in the understanding and treatment of addiction. Am. J. Addict. 12, S36–S47.
Patkar, A.A., Mannelli, P., Peindl, K., Hill, K.P., Wu, L.T., Lee, T., Kuhn, C., 2008.
Relationship of the serotonin transporter with prolactin response to metachlorophenylpiperazine in cocaine dependence. J. Psychiatr. Res. 42, 1213–1219.
Poduska, J.M., Kellam, S.G., Wang, W., Brown, C.H., Ialongo, N.S., Toyinbo, P., 2008.
Impact of the Good Behavior Game, a universal classroom-based behavior intervention, on young adult service use for problems with emotions, behavior, or
drugs or alcohol. Drug Alcohol Depend. 95 (Suppl. 1), S29–S44.
Reich, T., Cloninger, C.R., Van, E.P., Rice, J.P., Mullaney, J., 1988. Secular trends in the
familial transmission of alcoholism. Alcohol. Clin. Exp. Res. 12, 458–464.
Riley, B.B., Conrad, K.J., Bezruczko, N., Dennis, M.L., 2007. Relative precision, efficiency and construct validity of different starting and stopping rules for a
computerized adaptive test: the GAIN substance problem scale. J. Appl. Meas.,
8.
Romer, D., Walker, E.F., 2007. Adolescent Psychopathology and the Developing
Brain: Integrating Brain and Prevention Science. Oxford University Press, New
York.
Saha, T.D., Stinson, F.S., Grant, B.F., 2007. The role of alcohol consumption in future
classifications of alcohol use disorders. Drug Alcohol Depend. 89, 82–92.
Saxon, A.J., Oreskovich, M.R., Brkanac, Z., 2005. Genetic determinants of addiction
to opioids and cocaine. Harv. Rev. Psychiatry 13, 218–232.
Schuckit, M.A., 2009. An overview of genetic influences in alcoholism. J. Subst. Abuse
Treat. 36, S5–S14.
Schuckit, M.A., Goodwin, D.A., Winokur, G., 1972. A study of alcoholism in half
siblings. Am. J. Psychiatry 128, 1132–1136.
Sher, K.J., Bartholow, B.D., Wood, M.D., 2000. Personality and substance use disorders: a prospective study. J. Consult. Clin. Psychol. 68, 818–829.
Skinner, H.A., Allen, B.A., 1982. Alcohol dependence syndrome—measurement and
validation. J. Abnorm. Psychol. 91, 199–209.
Slutske, W.S., Heath, A.C., Madden, P.A., Bucholz, K.K., Statham, D.J., Martin, N.G.,
2002. Personality and the genetic risk for alcohol dependence. J. Abnorm. Psychol. 111, 124–133.
Stockwell, T., Hodgson, R., Edwards, G., Taylor, C., Rankin, H., 1979. Development
of a questionnaire to measure severity of alcohol dependence. Br. J. Addict. 74,
79–87.
Strong, D.R., Brown, R.A., Ramsey, S.E., Myers, M.G., 2003a. Nicotine dependence
measures among adolescents with psychiatric disorders: evaluating symptom
expression as a function of dependence severity. Nicotine Tob. Res. 5, 735–746.
Strong, D.R., Kahler, C.W., Abrantes, A.M., MacPherson, L., Myers, M.G., Ramsey, S.E.,
Brown, R.A., 2007. Nicotine dependence symptoms among adolescents with psychiatric disorders: using a Rasch model to evaluate symptom expression across
time. Nicotine Tob. Res. 9, 557–569.
Strong, D.R., Kahler, C.W., Colby, S.M., Griesler, P.C., Kandel, D., 2009. Linking measures of adolescent nicotine dependence to a common latent continuum. Drug
Alcohol Depend. 99, 296–308.
Strong, D.R., Kahler, C.W., Ramsey, S.E., Brown, R.A., 2003b. Finding order in the DSMIV nicotine dependence syndrome: a Rasch analysis. Drug Alcohol Depend. 72,
151–162.
Sutherland, G., Edwards, G., Taylor, C., Phillips, G., Gossop, M., Brady, R., 1986. The
measurement of opiate dependence. Br. J. Addict. 81, 485–494.
Swendsen, J.D., Conway, K.P., Rounsaville, B.J., Merikangas, K.R., 2002. Are personality traits familial risk factors for substance use disorders? Results of a controlled
family study. Am. J. Psychiatry 159, 1760–1766.
Tarter, R.E., Kirisci, L., Habeych, M., Reynolds, M., Vanyukov, M., 2004. Neurobehavior
disinhibition in childhood predisposes boys to substance use disorder by young
adulthood: direct and mediated etiologic pathways. Drug Alcohol Depend. 73,
121–132.
Tarter, R.E., Kirisci, L., Mezzich, A., Cornelius, J.R., Pajer, K., Vanyukov, M., Gardner, W.,
Blackson, T., Clark, D., 2003. Neurobehavioral disinhibition in childhood predicts
early age at onset of substance use disorder. Am. J. Psychiatry 160, 1078–1085.
Tarter, R.E., Laird, S.B., Kabene, M., Bukstein, O., Kaminer, Y., 1990. Drug-abuse severity in adolescents is associated with magnitude of deviation in temperament
traits. Br. J. Addict. 85, 1501–1504.
Tarter, R.E., Vanyukov, M., 1991. Theoretical and operational framework for research
into the etiology of substance use disorders. J. Child Adoles. Subst. Abuse 10,
1–12.
Tarter, R.E., Vanyukov, M., 1994. Alcoholism: a developmental disorder. J. Consult.
Clin. Psychol. 62, 1096–1107.
Tarter, R.E., Vanyukov, M., Kirisci, L., Reynolds, M., Clark, D.B., 2006. Predictors of
marijuana use in adolescents before and after licit drug use: examination of the
gateway hypothesis. Am. J. Psychiatry 163, 2134–2140.
Topp, L., Mattick, R.P., 1997. Validation of the amphetamine dependence syndrome
and the SamDQ. Addiction 92, 151–162.
Tsuang, M.T., Lyons, M.J., Eisen, S.A., Goldberg, J., True, W., Lin, N., Meyer, J.M.,
Toomey, R., Faraone, S.V., Eaves, L., 1996. Genetic influences on DSM-III-R drug
abuse and dependence: a study of 3,372 twin pairs. Am. J. Med. Genet. 67,
473–477.
Tsuang, M.T., Lyons, M.J., Meyer, J.M., Doyle, T., Eisen, S.A., Goldberg, J., True, W.,
Lin, N., Toomey, R., Eaves, L., 1998. Co-occurrence of abuse of different drugs in
men: the role of drug-specific and shared vulnerabilities. Arch. Gen. Psychiatry
55, 967–972.
Uhl, G.R., 2004. Molecular Genetics of Substance Abuse Vulnerability: Remarkable
Recent Convergence of Genome Scan Results. New York Acad Sciences, New
York.
Uhl, G.R., Drgon, T., Liu, Q.R., Johnson, C., Walther, D., Komiyama, T., Harano, M.,
Sekine, Y., Inada, T., Ozaki, N., Iyo, M., Iwata, N., Yamada, M., Sora, I., Chen, C.K., Liu,
H.C., Ujike, H., Lin, S.K., 2008. Genome-wide association for methamphetamine
dependence: convergent results from 2 samples. Arch. Gen. Psychiatry 65,
345–355.
Vanyukov, M., Kirisci, L., Moss, L., Tarter, R.E., Reynolds, M., Maher, B.S., Kirillova, G.P.,
Ridenour, T., Clark, D.B., 2009. Measurement of the risk for substance use disorders: phenotypic and genetic analysis of an index of common liability. Behav.
Genet. 39, 233–244.
Vanyukov, M.M., Kirisci, L., Tarter, R.E., Simkevitz, H.F., Kirillova, G.P., Maher, B.S.,
Clark, D.B., 2003a. Liability to substance use disorders: 2. A measurement
approach. Neurosci. Biobehav. Rev. 27, 517–526.
Vanyukov, M.M., Maher, B.S., Devlin, B., Kirillova, G.P., Kirisci, L., Yu, L.M., Ferrell, R.E.,
2007. The MAOA promoter polymorphism, disruptive behavior disorders, and
early onset substance use disorder: gene–environment interaction. Psychiatr.
Genet. 17, 323–332.
Vanyukov, M.M., Tarter, R.E., 2000. Genetic studies of substance abuse. Drug Alcohol
Depend. 59, 101–123.
Vanyukov, M.M., Tarter, R.E., Kirisci, L., Kirillova, G.P., Maher, B.S., Clark, D.B., 2003b.
Liability to substance use disorders: 1. Common mechanisms and manifestations. Neurosci. Biobehav. Rev. 27, 507–515.
Volkow, N.D., Fowler, J.S., Wang, G.J., 2003. The addicted human brain: insights from
imaging studies. J. Clin. Invest. 111, 1444–1451.
Volkow, N.D., Fowler, J.S., Wang, G.J., Swanson, J.M., 2004. Dopamine in drug abuse
and addiction: results from imaging studies and treatment implications. Mol.
Psychiatry 9, 557–569.
Volkow, N.D., Wang, G.J., Telang, F., Fowler, J.S., Logan, J., Childress, A.R.,
Jayne, M., Ma, Y., Wong, C., 2006. Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction. J. Neurosci. 26,
6583–6588.
Winokur, G., Reich, T., Rimmer, J., Pitts Jr., F.N., 1970. Alcoholism. 3. Diagnosis and
familial psychiatric illness in 259 alcoholic probands. Arch. Gen. Psychiatry 23,
104–111.
Wu, L.T., Pan, J.J., Blazer, D.G., Tai, B., Stitzer, M.L., Brooner, R.K., Woody, G.E., Patkar,
A.A., Blaine, J.D., 2009. An item response theory modeling of alcohol and marijuana dependences: A National Drug Abuse Treatment Clinical Trials Network
study. J. Stud. Alcohol Drugs 70, 414–425.
Young, S.E., Friedman, N.P., Miyake, A., Willcutt, E.G., Corley, R.P., Haberstick, B.C.,
Hewitt, J.K., 2009. Behavioral disinhibition: liability for externalizing spectrum
disorders and its genetic and environmental relation to response inhibition
across adolescence. J. Abnorm. Psychol. 118, 117–130.
Young, S.E., Stallings, M.C., Corley, R.P., Krauter, K.S., Hewitt, J.K., 2000. Genetic and
environmental influences on behavioral disinhibition. Am. J. Med. Genet. 96,
684–695.